function [J, grad] = costFunctionReg(theta, X, y, lambda)
%COSTFUNCTIONREG Compute cost and gradient for logistic regression with regularization
%   J = COSTFUNCTIONREG(theta, X, y, lambda) computes the cost of using
%   theta as the parameter for regularized logistic regression and the
%   gradient of the cost w.r.t. to the parameters. 

% Initialize some useful values
m = length(y); % number of training examples

% You need to return the following variables correctly 
J = 0;
grad = zeros(size(theta));

% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
%               You should set J to the cost.
%               Compute the partial derivatives and set grad to the partial
%               derivatives of the cost w.r.t. each parameter in theta

% 先计算无正则化的J
[J, grad] = costFunction(theta, X, y);
% 加上lambda / (2 * m) * (...)
n = size(theta);
temp = ones(1, n);
% 排除theta(0)
temp(1) = 0;
extral = temp * theta .^ 2 * lambda / (2 * m);
J = J + extral;

% grad加上lambda / m * theta(j)
for i = 2:n
    grad(i) = grad(i) + lambda / m * theta(i);
endfor


% =============================================================

end
